National Healthcare Quality and Disparities Report
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 25 of 174 Research Studies DisplayedZuvekas SH, Kashihara D
AHRQ Author: Zuvekas SH
The impacts of the COVID-19 pandemic on the Medical Expenditure Panel Survey.
The COVID-19 pandemic caused substantial disruptions in the field operations of all 3 major components of the Medical Expenditure Panel Survey (MEPS). In this study, the investigators described how the MEPS program successfully responded to these challenges by reengineering field operations, including survey modes, to complete data collection and maintain data release schedules.
AHRQ-authored.
Citation: Zuvekas SH, Kashihara D .
The impacts of the COVID-19 pandemic on the Medical Expenditure Panel Survey.
Am J Public Health 2021 Dec;111(12):2157-66. doi: 10.2105/ajph.2021.306534..
Keywords: Medical Expenditure Panel Survey (MEPS), COVID-19, Healthcare Costs, Data
Sangal RB, Fodeh S, Taylor A
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
Nontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. In this study, the investigators aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.
AHRQ-funded; HS023554.
Citation: Sangal RB, Fodeh S, Taylor A .
Identification of patients with nontraumatic intracranial hemorrhage using administrative claims data.
J Stroke Cerebrovasc Dis 2020 Dec;29(12):105306. doi: 10.1016/j.jstrokecerebrovasdis.2020.105306..
Keywords: Cardiovascular Conditions, Neurological Disorders, Diagnostic Safety and Quality, Data
Byrd TF, Ahmad FS, Liebovitz DM
Defragmenting heart failure care: medical records integration.
This article discusses the need to improve interoperability of software systems so that so that providers and patients can access clinical information needed to help coordinate care of heart failure patients. New data standards currently being proposed in legislation would make it possible to guide clinical decision-making.
AHRQ-funded; HS026385.
Citation: Byrd TF, Ahmad FS, Liebovitz DM .
Defragmenting heart failure care: medical records integration.
Heart Fail Clin 2020 Oct;16(4):467-77. doi: 10.1016/j.hfc.2020.06.007..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Heart Disease and Health, Cardiovascular Conditions, Data
Lin JS, Murad MH, Leas B
A narrative review and proposed framework for using health system data with systematic reviews to support decision-making.
This paper addresses when and how the use of health system data might make systematic reviews more useful to decisionmakers. The authors have developed a framework to guide the use of health system data alongside systematic reviews based on a narrative review of the literature and empirical experience. They recommend future methodological work on how best to handle internal and external validity concerns of health system data in the context of systematically reviewed data and work on developing infrastructure to do this type of work.
AHRQ-funded; 290201500007I; 29032001T05; 290201500005I; 290201500009I.
Citation: Lin JS, Murad MH, Leas B .
A narrative review and proposed framework for using health system data with systematic reviews to support decision-making.
J Gen Intern Med 2020 Jun;35(6):1830-35. doi: 10.1007/s11606-020-05783-5..
Keywords: Learning Health Systems, Health Systems, Evidence-Based Practice, Data, Shared Decision Making
Dixon BE, Wen C, French T
Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI).
The authors extended the open-source software Observational Health Data Sciences and Informatics (OHDSI) to incorporate new functions useful for population health. They developed and tested methods to measure the completeness, timeliness and entropy of information; timeliness was not adopted as its context did not fit with the existing OHDSI domains. The case report examined the process and reasons for acceptance and rejection of ideas proposed to an open-source community like OHDSI.
AHRQ-funded; HS025502.
Citation: Dixon BE, Wen C, French T .
Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI).
BMJ Health Care Inform 2020 Mar;27(1). doi: 10.1136/bmjhci-2019-100054..
Keywords: Public Health, Data
Jarrin OF, Nyandege AN, Grafova IB
Validity of race and ethnicity codes in Medicare administrative data compared with gold-standard self-reported race collected during routine home health care visits.
The authors compared the validity of two race/ethnicity variables found in Medicare administrative data against a gold-standard source also available in the Medicare data warehouse. They found that the race/ethnicity variables contained in Medicare administrative data for minority health disparities research can be improved through the use of self-reported race/ethnicity data. They conclude that future work to improve the accuracy of Medicare beneficiaries' race/ethnicity data should incorporate and augment the self-reported race/ethnicity data contained in assessment and survey data, available within the Medicare data warehouse.
AHRQ-funded; HS022406.
Citation: Jarrin OF, Nyandege AN, Grafova IB .
Validity of race and ethnicity codes in Medicare administrative data compared with gold-standard self-reported race collected during routine home health care visits.
Med Care 2020 Jan;58(1):e1-e8. doi: 10.1097/mlr.0000000000001216..
Keywords: Racial and Ethnic Minorities, Home Healthcare, Medicare, Data, Disparities, Research Methodologies
Saldanha IJ, Smith BT, Ntzani E
The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research.
Funded by the US Agency for Healthcare Research and Quality (AHRQ), the Systematic Review Data Repository (SRDR) is a free, web-based, open-source, data management and archival platform for reviews. The objectives of this study were to describe (1) the current extent of usage of SRDR and (2) the characteristics of all projects with publicly available data on the SRDR website.
AHRQ-funded; HHSA290201500002I_HHSA29032012T.
Citation: Saldanha IJ, Smith BT, Ntzani E .
The Systematic Review Data Repository (SRDR): descriptive characteristics of publicly available data and opportunities for research.
Syst Rev 2019 Dec 20;8(1):334. doi: 10.1186/s13643-019-1250-y..
Keywords: Evidence-Based Practice, Data, Research Methodologies, Registries
Boudreaux M, Gangopadhyaya A, Long SK
AHRQ Author: Karaca Z
Using data from the Healthcare Cost and Utilization Project for state health policy research.
Investigators describe the opportunities and challenges of using HCUP data to conduct state health policy research and to provide empirical examples of what can go wrong when using the national HCUP data inappropriately. Analyzing cesarean delivery rates, discharges per capita, and discharges by the payer, they found that state-level estimates are volatile and often provide misleading policy conclusions. They conclude that the Nationwide Inpatient Sample should not be used for state-level research and specified that AHRQ provides resources to assist analysts with state-specific studies using State Inpatient Database files.
AHRQ-authored.
Citation: Boudreaux M, Gangopadhyaya A, Long SK .
Using data from the Healthcare Cost and Utilization Project for state health policy research.
Med Care 2019 Nov;57(11):855-60. doi: 10.1097/mlr.0000000000001196..
Keywords: Healthcare Cost and Utilization Project (HCUP), Policy, Health Services Research (HSR), Healthcare Costs, Data, Research Methodologies
Shen NT, Salajegheh A, Brown RS
A call to standardize definitions, data collection, and outcome assessment to improve care in alcohol-related liver disease.
Alcohol-related liver disease (ALD) is highly prevalent and appears to be increasingly reported with worsening mortality; thus, optimizing care in this patient population is imperative. This requires a multidisciplinary, multifaceted approach that includes recognizing alcohol use disorder (AUD) and existing treatments for AUD. In this paper, the authors call for standardizing definitions, data collection, and outcome assessment to improve care in alcohol-related liver disease.
AHRQ-funded; HS000066.
Citation: Shen NT, Salajegheh A, Brown RS .
A call to standardize definitions, data collection, and outcome assessment to improve care in alcohol-related liver disease.
Hepatology 2019 Sep;70(3):1038-44. doi: 10.1002/hep.30587..
Keywords: Data, Alcohol Use, Outcomes
Bacon E, Budney G, Bondy J
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
This article describes attributes of regional distributed data networks using electronic health records (EHR) data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. The authors indicate that while benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.
AHRQ-funded; HS0122143.
Citation: Bacon E, Budney G, Bondy J .
Developing a regional distributed data network for surveillance of chronic health conditions: the Colorado Health Observation Regional Data Service.
J Public Health Manag Pract 2019 Sep/Oct;25(5):498-507. doi: 10.1097/phh.0000000000000810..
Keywords: Chronic Conditions, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Public Health
Liu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Wang E, Kang H, Gong Y
Generating a health information technology event database from FDA MAUDE reports.
This study examined using a health information technology (HIT) event database to identify patient safety events (PSEs) or medical errors. The study used the FDA Manufacturer and User Facility Device Experience (MAUDE) database to extract HIT events. Classic and CNN models were utilized on a test set. The model was capable of identifying HIT event with about a 90% accuracy.
AHRQ-funded; HS022895.
Citation: Wang E, Kang H, Gong Y .
Generating a health information technology event database from FDA MAUDE reports.
Stud Health Technol Inform 2019 Aug 21;264:883-87. doi: 10.3233/shti190350..
Keywords: Health Information Technology (HIT), Medical Devices, Adverse Events, Data, Medical Errors, Patient Safety
Yao B, Kang H, Gong Y
Data quality assessment of narrative medication error reports.
This study examined the data quality of patient safety event (PSE) reports that are used to analyze the root causes of PSE. If the data quality is poor then the reporting and root cause analysis (RCA) will also be poor. Incomplete or missing data is the most prevalent problem in these reports. The researchers used an adapted taxonomy to assess the data quality of PSE reports, and extracted sample reports based on eight error types. The extracts were scored by experts. They found that most structured fields were ignored by reporters, but the narrative parts of the reports contained rich and valuable information. The results show that the adapted taxonomy could be a promising tool for report quality assessment and improvement.
AHRQ-funded; HS022895.
Citation: Yao B, Kang H, Gong Y .
Data quality assessment of narrative medication error reports.
Stud Health Technol Inform 2019 Aug 9;265:101-06. doi: 10.3233/shti190146..
Keywords: Adverse Drug Events (ADE), Medication, Medical Errors, Adverse Events, Data, Patient Safety
Polubriaginof FCG, Ryan P, Salmasian H
Challenges with quality of race and ethnicity data in observational databases.
This study assessed the quality of race and ethnicity information in observational health databases as well as electronic health records (EHRs) and to propose patient self-recording as a way to improve accuracy. Data from the Healthcare Cost and Utilization Project (HCUP) and Optum Labs, and from a single New York City healthcare system’s EHR was compared. Among 160 million patients in the HCUP database, no race or ethnicity data was recorded for 25% of the records. Among the 2.4 million patients in the New York City HER, race or ethnicity was unknown for 57%. However, when patients were allowed to directly record their race and ethnicity, percentages rose to 86%.
AHRQ-funded; HS021816; HS023704; HS024713.
Citation: Polubriaginof FCG, Ryan P, Salmasian H .
Challenges with quality of race and ethnicity data in observational databases.
J Am Med Inform Assoc 2019 Aug;26(8-9):730-36. doi: 10.1093/jamia/ocz113..
Keywords: Healthcare Cost and Utilization Project (HCUP), Data, Racial and Ethnic Minorities, Electronic Health Records (EHRs), Health Information Technology (HIT), Health Services Research (HSR)
Lewis VA, Joynet Maddox K, Austin AM
Developing and validating a measure to estimate poverty in Medicare administrative data.
The purpose of this study was to develop and validate a measure that estimates individual level poverty in Medicare administrative data that can be used in studies of Medicare claims. The investigators indicate that a poverty score can be calculated using Medicare administrative data for use as a continuous or binary measure and that this measure can improve researchers' ability to identify poverty in Medicare administrative data.
AHRQ-funded; HS024075.
Citation: Lewis VA, Joynet Maddox K, Austin AM .
Developing and validating a measure to estimate poverty in Medicare administrative data.
Med Care 2019 Aug;57(8):601-07. doi: 10.1097/mlr.0000000000001154..
Keywords: Medicare, Data, Low-Income, Research Methodologies
Li X, Fireman BH, Curtis JR
Validity of privacy-protecting analytical methods that use only aggregate-level information to conduct multivariable-adjusted analysis in distributed data networks.
Researchers analyzed the impact of using distributed data networks to conduct large-scale epidemiologic studies on protecting privacy of the subjects. Three aggregate-level data-sharing approaches were tested (risk-set, summary-table, and effect-estimate). Four confounding adjustment methods (matching, stratification, inverse probability matching, and matching weighting) and 2 summary scores (propensity and disease risk) for binary and time-to-event-outcomes were assessed. Risk-set data sharing generally performed better than summary-table and effect-estimate data-sharing which often produced discrepancies in settings with rare outcomes and small sample sizes.
AHRQ-funded; HS026214.
Citation: Li X, Fireman BH, Curtis JR .
Validity of privacy-protecting analytical methods that use only aggregate-level information to conduct multivariable-adjusted analysis in distributed data networks.
Am J Epidemiol 2019 Apr;188(4):709-23. doi: 10.1093/aje/kwy265..
Keywords: Data, Research Methodologies
Tong BC, Kim S, Kosinski A
Penetration, completeness, and representativeness of the Society of Thoracic Surgeons General Thoracic Surgery Database for lobectomy.
Not all surgeons performing lobectomy in the United States report outcomes to The Society of Thoracic Surgeons General Thoracic Surgery Database (STS GTSD). In this study, the investigators examined penetration, completeness, and representativeness of the STS GTSD for lobectomy in the Centers for Medicare and Medicaid Services (CMS) patient population. The investigators concluded that participation in the STS GTSD increased over time, but penetration lagged behind that of the other STS National Databases.
AHRQ-funded; HS022279.
Citation: Tong BC, Kim S, Kosinski A .
Penetration, completeness, and representativeness of the Society of Thoracic Surgeons General Thoracic Surgery Database for lobectomy.
Ann Thorac Surg 2019 Mar;107(3):897-902. doi: 10.1016/j.athoracsur.2018.07.059..
Keywords: Surgery, Cancer: Lung Cancer, Cancer, Data, Provider: Physician, Provider
Lindell RB, Nishisaki A, Weiss SL
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
This study compared the use of Virtual Pediatric Systems with traditional use of International Classification of Diseases, 9th edition (ICD) to identify children with severe sepsis or septic shock in PICU settings. Two different systems were compared “Martin” and “Angus”. Both showed good agreement, but ICD9 identified a smaller more accurate cohort of children. Additional analysis of discrepancies between the reference standard the two virtual systems showed that prospective screening missed 66 patients who were diagnosed with severe sepsis or severe shock. Once they were included in the standard cohort, agreement improved with a positive predictive value of 70%.
AHRQ-funded; HS024511; HS022464.
Citation: Lindell RB, Nishisaki A, Weiss SL .
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
Crit Care Med 2019 Feb;47(2):e129-e35. doi: 10.1097/ccm.0000000000003541..
Keywords: Children/Adolescents, Intensive Care Unit (ICU), Data, Sepsis
Hsu YJ, Kosinski AS, Wallace AS
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
The authors assessed the utility of using external databases for quality improvement (QI) evaluations in the context of an innovative QI collaborative aimed to reduce three infections and improve patient safety across the cardiac surgery service line. They compared changes in each outcome between 15 intervention hospitals and 52 propensity score-matched hospitals, and found that improvement trends in several outcomes among the studied intervention hospitals were not statistically different from those in comparison hospitals. They conclude that using external databases may permit comparative effectiveness assessment by providing concurrent comparison groups, additional outcome measures, and longer follow-up.
AHRQ-funded; HS019934.
Citation: Hsu YJ, Kosinski AS, Wallace AS .
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
J Comp Eff Res 2019 Jan;8(1):21-32. doi: 10.2217/cer-2018-0051..
Keywords: Patient Safety, Quality Improvement, Quality Indicators (QIs), Quality of Care, Surgery, Cardiovascular Conditions, Comparative Effectiveness, Data, Hospitals, Research Methodologies, Patient-Centered Outcomes Research
Yang Y, Bass EJ, Sockolow PS
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
Researchers elicit knowledge related to expert decision-making processes to inform information technology design and related interventions. In this study, the investigators examine knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization. The investigators concluded that the data collection and validation methodology showed promise for knowledge elicitation in time-constrained situations.
AHRQ-funded; HS024537.
Citation: Yang Y, Bass EJ, Sockolow PS .
Knowledge elicitation of homecare admission decision making processes via focus group, member checking and data visualization.
AMIA Annu Symp Proc 2018 Dec 5;2018:1127-36..
Keywords: Home Healthcare, Shared Decision Making, Health Information Technology (HIT), Data
Ross JS, Waldstreicher J, Bamford S
Overview and experience of the YODA Project with clinical trial data sharing after 5 years.
This article provides an overview of the Yale University Open Data Access (YODA) Project, which has facilitated access to clinical trial data since 2013. The Project’s key decisions to establish data sharing policies are described, and the authors suggest how their experience and the experiences of their data-generator partners can be used to enhance other ongoing or future initiatives.
AHRQ-funded; HS022882; HS025164.
Citation: Ross JS, Waldstreicher J, Bamford S .
Overview and experience of the YODA Project with clinical trial data sharing after 5 years.
Sci Data 2018 Nov 27;5:180268. doi: 10.1038/sdata.2018.268..
Keywords: Data, Research Methodologies
Newgard CD, Malveau S, Zive D
Building a longitudinal cohort from 9-1-1 to 1-year using existing data sources, probabilistic linkage, and multiple imputation: a validation study.
The objective of this seven-county study was to describe and validate construction of a population-based, longitudinal cohort of injured older adults from 9-1-1 call to 1-year follow-up. Results showed that a population-based emergency care cohort with long-term outcomes can be constructed from existing data sources with high accuracy and reasonable validity of resulting variables.
AHRQ-funded; HS023796.
Citation: Newgard CD, Malveau S, Zive D .
Building a longitudinal cohort from 9-1-1 to 1-year using existing data sources, probabilistic linkage, and multiple imputation: a validation study.
Acad Emerg Med 2018 Nov;25(11):1268-83. doi: 10.1111/acem.13512..
Keywords: Data, Research Methodologies, Elderly, Emergency Department, Injuries and Wounds
Wang SV, Maro JC, Baro E
Data mining for adverse drug events with a propensity score-matched tree-based scan statistic.
In this study, the investigators propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. They subsequently conducted plasmode simulations to evaluate performance. The authors suggest that TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events.
AHRQ-funded; HS022193.
Citation: Wang SV, Maro JC, Baro E .
Data mining for adverse drug events with a propensity score-matched tree-based scan statistic.
Epidemiology 2018 Nov;29(6):895-903. doi: 10.1097/ede.0000000000000907..
Keywords: Adverse Drug Events (ADE), Adverse Events, Patient Safety, Medication, Medication: Safety, Data, Research Methodologies
Smith S, Snyder A, McMahon LF
Success in hospital-acquired pressure ulcer prevention: a tale in two data sets.
This study assessed hospital-acquired pressure ulcer (HAPU) incidence, severity, and trends using administrative data for 2009-14 from three states. The HAPU incidence the investigators found was approximately one-twentieth of that found in chart-based surveillance review data. The authors suggest that transitioning from administrative data to chart-based surveillance review to measure HAPUs and accounting for HAPU severity could improve the validity of HAPU measures for assessing the clinical and financial impact of value-based purchasing interventions.
AHRQ-funded; HS018334; HS019767.
Citation: Smith S, Snyder A, McMahon LF .
Success in hospital-acquired pressure ulcer prevention: a tale in two data sets.
Health Aff 2018 Nov;37(11):1787-96. doi: 10.1377/hlthaff.2018.0712.
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Keywords: Data, Healthcare Cost and Utilization Project (HCUP), Pressure Ulcers, Prevention
Fong A, Adams KT, Gaunt MJ
Identifying health information technology related safety event reports from patient safety event report databases.
The objective of this paper was to identify health information technology (HIT) related events from patient safety event (PSE) report free-text descriptions. A difference-based scoring approach was used to prioritize and select model features. A feature-constraint model was developed and evaluated to support the analysis of PSE reports. The feature-constraint model provides a method to identify HIT-related patient safety hazards using a method that is applicable across healthcare systems with variability in their PSE report structures.
AHRQ-funded; HS023701.
Citation: Fong A, Adams KT, Gaunt MJ .
Identifying health information technology related safety event reports from patient safety event report databases.
J Biomed Inform 2018 Oct;86:135-42. doi: 10.1016/j.jbi.2018.09.007..
Keywords: Health Information Technology (HIT), Patient Safety, Adverse Events, Data